// sherpa-onnx/csrc/online-lstm-transducer-model.cc
//
// Copyright (c)  2023  Xiaomi Corporation
#include "sherpa-onnx/csrc/online-lstm-transducer-model.h"

#include <assert.h>

#include <algorithm>
#include <memory>
#include <sstream>
#include <string>
#include <utility>
#include <vector>

#if __ANDROID_API__ >= 9
#include "android/asset_manager.h"
#include "android/asset_manager_jni.h"
#endif

#include "onnxruntime_cxx_api.h"  // NOLINT
#include "sherpa-onnx/csrc/cat.h"
#include "sherpa-onnx/csrc/macros.h"
#include "sherpa-onnx/csrc/online-transducer-decoder.h"
#include "sherpa-onnx/csrc/onnx-utils.h"
#include "sherpa-onnx/csrc/session.h"
#include "sherpa-onnx/csrc/unbind.h"

namespace sherpa_onnx {

OnlineLstmTransducerModel::OnlineLstmTransducerModel(
    const OnlineModelConfig &config)
    : env_(ORT_LOGGING_LEVEL_WARNING),
      config_(config),
      sess_opts_(GetSessionOptions(config)),
      allocator_{} {
  {
    auto buf = ReadFile(config.transducer.encoder);
    InitEncoder(buf.data(), buf.size());
  }

  {
    auto buf = ReadFile(config.transducer.decoder);
    InitDecoder(buf.data(), buf.size());
  }

  {
    auto buf = ReadFile(config.transducer.joiner);
    InitJoiner(buf.data(), buf.size());
  }
}

#if __ANDROID_API__ >= 9
OnlineLstmTransducerModel::OnlineLstmTransducerModel(
    AAssetManager *mgr, const OnlineModelConfig &config)
    : env_(ORT_LOGGING_LEVEL_WARNING),
      config_(config),
      sess_opts_(GetSessionOptions(config)),
      allocator_{} {
  {
    auto buf = ReadFile(mgr, config.transducer.encoder);
    InitEncoder(buf.data(), buf.size());
  }

  {
    auto buf = ReadFile(mgr, config.transducer.decoder);
    InitDecoder(buf.data(), buf.size());
  }

  {
    auto buf = ReadFile(mgr, config.transducer.joiner);
    InitJoiner(buf.data(), buf.size());
  }
}
#endif

void OnlineLstmTransducerModel::InitEncoder(void *model_data,
                                            size_t model_data_length) {
  encoder_sess_ = std::make_unique<Ort::Session>(env_, model_data,
                                                 model_data_length, sess_opts_);

  GetInputNames(encoder_sess_.get(), &encoder_input_names_,
                &encoder_input_names_ptr_);

  GetOutputNames(encoder_sess_.get(), &encoder_output_names_,
                 &encoder_output_names_ptr_);

  // get meta data
  Ort::ModelMetadata meta_data = encoder_sess_->GetModelMetadata();
  if (config_.debug) {
    std::ostringstream os;
    os << "---encoder---\n";
    PrintModelMetadata(os, meta_data);
    SHERPA_ONNX_LOGE("%s", os.str().c_str());
  }

  Ort::AllocatorWithDefaultOptions allocator;  // used in the macro below
  SHERPA_ONNX_READ_META_DATA(num_encoder_layers_, "num_encoder_layers");
  SHERPA_ONNX_READ_META_DATA(T_, "T");
  SHERPA_ONNX_READ_META_DATA(decode_chunk_len_, "decode_chunk_len");
  SHERPA_ONNX_READ_META_DATA(rnn_hidden_size_, "rnn_hidden_size");
  SHERPA_ONNX_READ_META_DATA(d_model_, "d_model");
}

void OnlineLstmTransducerModel::InitDecoder(void *model_data,
                                            size_t model_data_length) {
  decoder_sess_ = std::make_unique<Ort::Session>(env_, model_data,
                                                 model_data_length, sess_opts_);

  GetInputNames(decoder_sess_.get(), &decoder_input_names_,
                &decoder_input_names_ptr_);

  GetOutputNames(decoder_sess_.get(), &decoder_output_names_,
                 &decoder_output_names_ptr_);

  // get meta data
  Ort::ModelMetadata meta_data = decoder_sess_->GetModelMetadata();
  if (config_.debug) {
    std::ostringstream os;
    os << "---decoder---\n";
    PrintModelMetadata(os, meta_data);
    SHERPA_ONNX_LOGE("%s", os.str().c_str());
  }

  Ort::AllocatorWithDefaultOptions allocator;  // used in the macro below
  SHERPA_ONNX_READ_META_DATA(vocab_size_, "vocab_size");
  SHERPA_ONNX_READ_META_DATA(context_size_, "context_size");
}

void OnlineLstmTransducerModel::InitJoiner(void *model_data,
                                           size_t model_data_length) {
  joiner_sess_ = std::make_unique<Ort::Session>(env_, model_data,
                                                model_data_length, sess_opts_);

  GetInputNames(joiner_sess_.get(), &joiner_input_names_,
                &joiner_input_names_ptr_);

  GetOutputNames(joiner_sess_.get(), &joiner_output_names_,
                 &joiner_output_names_ptr_);

  // get meta data
  Ort::ModelMetadata meta_data = joiner_sess_->GetModelMetadata();
  if (config_.debug) {
    std::ostringstream os;
    os << "---joiner---\n";
    PrintModelMetadata(os, meta_data);
    SHERPA_ONNX_LOGE("%s", os.str().c_str());
  }
}

std::vector<Ort::Value> OnlineLstmTransducerModel::StackStates(
    const std::vector<std::vector<Ort::Value>> &states) const {
  int32_t batch_size = static_cast<int32_t>(states.size());

  std::vector<const Ort::Value *> h_buf(batch_size);
  std::vector<const Ort::Value *> c_buf(batch_size);

  for (int32_t i = 0; i != batch_size; ++i) {
    assert(states[i].size() == 2);
    h_buf[i] = &states[i][0];
    c_buf[i] = &states[i][1];
  }

  Ort::Value h = Cat(allocator_, h_buf, 1);
  Ort::Value c = Cat(allocator_, c_buf, 1);

  std::vector<Ort::Value> ans;
  ans.reserve(2);
  ans.push_back(std::move(h));
  ans.push_back(std::move(c));

  return ans;
}

std::vector<std::vector<Ort::Value>> OnlineLstmTransducerModel::UnStackStates(
    const std::vector<Ort::Value> &states) const {
  int32_t batch_size = states[0].GetTensorTypeAndShapeInfo().GetShape()[1];
  assert(states.size() == 2);

  std::vector<std::vector<Ort::Value>> ans(batch_size);

  std::vector<Ort::Value> h_vec = Unbind(allocator_, &states[0], 1);
  std::vector<Ort::Value> c_vec = Unbind(allocator_, &states[1], 1);

  assert(h_vec.size() == batch_size);
  assert(c_vec.size() == batch_size);

  for (int32_t i = 0; i != batch_size; ++i) {
    ans[i].push_back(std::move(h_vec[i]));
    ans[i].push_back(std::move(c_vec[i]));
  }

  return ans;
}

std::vector<Ort::Value> OnlineLstmTransducerModel::GetEncoderInitStates() {
  // Please see
  // https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/lstm_transducer_stateless2/export-onnx.py#L185
  // for details
  constexpr int32_t kBatchSize = 1;
  std::array<int64_t, 3> h_shape{num_encoder_layers_, kBatchSize, d_model_};
  Ort::Value h = Ort::Value::CreateTensor<float>(allocator_, h_shape.data(),
                                                 h_shape.size());

  Fill<float>(&h, 0);

  std::array<int64_t, 3> c_shape{num_encoder_layers_, kBatchSize,
                                 rnn_hidden_size_};

  Ort::Value c = Ort::Value::CreateTensor<float>(allocator_, c_shape.data(),
                                                 c_shape.size());

  Fill<float>(&c, 0);

  std::vector<Ort::Value> states;

  states.reserve(2);
  states.push_back(std::move(h));
  states.push_back(std::move(c));

  return states;
}

std::pair<Ort::Value, std::vector<Ort::Value>>
OnlineLstmTransducerModel::RunEncoder(Ort::Value features,
                                      std::vector<Ort::Value> states,
                                      Ort::Value /* processed_frames */) {
  std::array<Ort::Value, 3> encoder_inputs = {
      std::move(features), std::move(states[0]), std::move(states[1])};

  auto encoder_out = encoder_sess_->Run(
      {}, encoder_input_names_ptr_.data(), encoder_inputs.data(),
      encoder_inputs.size(), encoder_output_names_ptr_.data(),
      encoder_output_names_ptr_.size());

  std::vector<Ort::Value> next_states;
  next_states.reserve(2);
  next_states.push_back(std::move(encoder_out[1]));
  next_states.push_back(std::move(encoder_out[2]));

  return {std::move(encoder_out[0]), std::move(next_states)};
}

Ort::Value OnlineLstmTransducerModel::RunDecoder(Ort::Value decoder_input) {
  auto decoder_out = decoder_sess_->Run(
      {}, decoder_input_names_ptr_.data(), &decoder_input, 1,
      decoder_output_names_ptr_.data(), decoder_output_names_ptr_.size());
  return std::move(decoder_out[0]);
}

Ort::Value OnlineLstmTransducerModel::RunJoiner(Ort::Value encoder_out,
                                                Ort::Value decoder_out) {
  std::array<Ort::Value, 2> joiner_input = {std::move(encoder_out),
                                            std::move(decoder_out)};
  auto logit =
      joiner_sess_->Run({}, joiner_input_names_ptr_.data(), joiner_input.data(),
                        joiner_input.size(), joiner_output_names_ptr_.data(),
                        joiner_output_names_ptr_.size());

  return std::move(logit[0]);
}

}  // namespace sherpa_onnx